{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Preclose</th>\n",
       "      <th>Open</th>\n",
       "      <th>Highest</th>\n",
       "      <th>Lowest</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Money</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990/12/19</td>\n",
       "      <td></td>\n",
       "      <td>96.050</td>\n",
       "      <td>99.980</td>\n",
       "      <td>95.790</td>\n",
       "      <td>99.980</td>\n",
       "      <td>126000.00</td>\n",
       "      <td>4.940000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1990/12/20</td>\n",
       "      <td>99.98</td>\n",
       "      <td>104.300</td>\n",
       "      <td>104.390</td>\n",
       "      <td>99.980</td>\n",
       "      <td>104.390</td>\n",
       "      <td>19700.00</td>\n",
       "      <td>8.400000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1990/12/21</td>\n",
       "      <td>104.39</td>\n",
       "      <td>109.070</td>\n",
       "      <td>109.130</td>\n",
       "      <td>103.730</td>\n",
       "      <td>109.130</td>\n",
       "      <td>2800.00</td>\n",
       "      <td>1.600000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990/12/24</td>\n",
       "      <td>109.13</td>\n",
       "      <td>113.570</td>\n",
       "      <td>114.550</td>\n",
       "      <td>109.130</td>\n",
       "      <td>114.550</td>\n",
       "      <td>3200.00</td>\n",
       "      <td>3.100000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1990/12/25</td>\n",
       "      <td>114.55</td>\n",
       "      <td>120.090</td>\n",
       "      <td>120.250</td>\n",
       "      <td>114.550</td>\n",
       "      <td>120.250</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>6.000000e+03</td>\n",
       "    </tr>\n",
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       "      <td>2024/9/24</td>\n",
       "      <td>2748.918</td>\n",
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       "      <th>8247</th>\n",
       "      <td>2024/9/25</td>\n",
       "      <td>2863.126</td>\n",
       "      <td>2901.419</td>\n",
       "      <td>2952.451</td>\n",
       "      <td>2889.048</td>\n",
       "      <td>2896.306</td>\n",
       "      <td>5682598.16</td>\n",
       "      <td>5.166981e+07</td>\n",
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       "      <th>8248</th>\n",
       "      <td>2024/9/26</td>\n",
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       "      <td>2889.014</td>\n",
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       "      <td>5763192.61</td>\n",
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       "      <th>8249</th>\n",
       "      <td>2024/9/27</td>\n",
       "      <td>3000.953</td>\n",
       "      <td>3049.103</td>\n",
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       "      <td>3017.445</td>\n",
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       "      <td>4922871.63</td>\n",
       "      <td>4.806126e+07</td>\n",
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       "      <th>8250</th>\n",
       "      <td>2024/9/30</td>\n",
       "      <td>3087.529</td>\n",
       "      <td>3194.722</td>\n",
       "      <td>3358.588</td>\n",
       "      <td>3153.697</td>\n",
       "      <td>3336.497</td>\n",
       "      <td>11023379.17</td>\n",
       "      <td>1.167773e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8251 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Day  Preclose      Open   Highest    Lowest     Close  \\\n",
       "0     1990/12/19              96.050    99.980    95.790    99.980   \n",
       "1     1990/12/20     99.98   104.300   104.390    99.980   104.390   \n",
       "2     1990/12/21    104.39   109.070   109.130   103.730   109.130   \n",
       "3     1990/12/24    109.13   113.570   114.550   109.130   114.550   \n",
       "4     1990/12/25    114.55   120.090   120.250   114.550   120.250   \n",
       "...          ...       ...       ...       ...       ...       ...   \n",
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       "8248   2024/9/26  2896.306  2893.745  3000.953  2889.014  3000.953   \n",
       "8249   2024/9/27  3000.953  3049.103  3087.529  3017.445  3087.529   \n",
       "8250   2024/9/30  3087.529  3194.722  3358.588  3153.697  3336.497   \n",
       "\n",
       "           Volume         Money  \n",
       "0       126000.00  4.940000e+05  \n",
       "1        19700.00  8.400000e+04  \n",
       "2         2800.00  1.600000e+04  \n",
       "3         3200.00  3.100000e+04  \n",
       "4         1500.00  6.000000e+03  \n",
       "...           ...           ...  \n",
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       "8249   4922871.63  4.806126e+07  \n",
       "8250  11023379.17  1.167773e+08  \n",
       "\n",
       "[8251 rows x 8 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "data=pd.read_csv('000001.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Day</th>\n",
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       "      <th>1</th>\n",
       "      <td>1990/12/20</td>\n",
       "      <td>104.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>114.550</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>120.250</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "             Day     Close\n",
       "0     1990/12/19    99.980\n",
       "1     1990/12/20   104.390\n",
       "2     1990/12/21   109.130\n",
       "3     1990/12/24   114.550\n",
       "4     1990/12/25   120.250\n",
       "...          ...       ...\n",
       "8246   2024/9/24  2863.126\n",
       "8247   2024/9/25  2896.306\n",
       "8248   2024/9/26  3000.953\n",
       "8249   2024/9/27  3087.529\n",
       "8250   2024/9/30  3336.497\n",
       "\n",
       "[8251 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data[[\"Day\",\"Close\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Day</th>\n",
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       "      <td>1990-12-19</td>\n",
       "      <td></td>\n",
       "      <td>96.050</td>\n",
       "      <td>99.980</td>\n",
       "      <td>95.790</td>\n",
       "      <td>99.980</td>\n",
       "      <td>126000.00</td>\n",
       "      <td>4.940000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1990-12-20</td>\n",
       "      <td>99.98</td>\n",
       "      <td>104.300</td>\n",
       "      <td>104.390</td>\n",
       "      <td>99.980</td>\n",
       "      <td>104.390</td>\n",
       "      <td>19700.00</td>\n",
       "      <td>8.400000e+04</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1990-12-21</td>\n",
       "      <td>104.39</td>\n",
       "      <td>109.070</td>\n",
       "      <td>109.130</td>\n",
       "      <td>103.730</td>\n",
       "      <td>109.130</td>\n",
       "      <td>2800.00</td>\n",
       "      <td>1.600000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990-12-24</td>\n",
       "      <td>109.13</td>\n",
       "      <td>113.570</td>\n",
       "      <td>114.550</td>\n",
       "      <td>109.130</td>\n",
       "      <td>114.550</td>\n",
       "      <td>3200.00</td>\n",
       "      <td>3.100000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1990-12-25</td>\n",
       "      <td>114.55</td>\n",
       "      <td>120.090</td>\n",
       "      <td>120.250</td>\n",
       "      <td>114.550</td>\n",
       "      <td>120.250</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>6.000000e+03</td>\n",
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       "      <th>8246</th>\n",
       "      <td>2024-09-24</td>\n",
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       "      <td>4776195.45</td>\n",
       "      <td>4.427953e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8247</th>\n",
       "      <td>2024-09-25</td>\n",
       "      <td>2863.126</td>\n",
       "      <td>2901.419</td>\n",
       "      <td>2952.451</td>\n",
       "      <td>2889.048</td>\n",
       "      <td>2896.306</td>\n",
       "      <td>5682598.16</td>\n",
       "      <td>5.166981e+07</td>\n",
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       "    <tr>\n",
       "      <th>8248</th>\n",
       "      <td>2024-09-26</td>\n",
       "      <td>2896.306</td>\n",
       "      <td>2893.745</td>\n",
       "      <td>3000.953</td>\n",
       "      <td>2889.014</td>\n",
       "      <td>3000.953</td>\n",
       "      <td>5763192.61</td>\n",
       "      <td>5.246691e+07</td>\n",
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       "    <tr>\n",
       "      <th>8249</th>\n",
       "      <td>2024-09-27</td>\n",
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       "      <td>3049.103</td>\n",
       "      <td>3087.529</td>\n",
       "      <td>3017.445</td>\n",
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       "      <td>4922871.63</td>\n",
       "      <td>4.806126e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8250</th>\n",
       "      <td>2024-09-30</td>\n",
       "      <td>3087.529</td>\n",
       "      <td>3194.722</td>\n",
       "      <td>3358.588</td>\n",
       "      <td>3153.697</td>\n",
       "      <td>3336.497</td>\n",
       "      <td>11023379.17</td>\n",
       "      <td>1.167773e+08</td>\n",
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       "</table>\n",
       "<p>8251 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "            Day  Preclose      Open   Highest    Lowest     Close  \\\n",
       "0    1990-12-19              96.050    99.980    95.790    99.980   \n",
       "1    1990-12-20     99.98   104.300   104.390    99.980   104.390   \n",
       "2    1990-12-21    104.39   109.070   109.130   103.730   109.130   \n",
       "3    1990-12-24    109.13   113.570   114.550   109.130   114.550   \n",
       "4    1990-12-25    114.55   120.090   120.250   114.550   120.250   \n",
       "...         ...       ...       ...       ...       ...       ...   \n",
       "8246 2024-09-24  2748.918  2770.754  2863.152  2761.372  2863.126   \n",
       "8247 2024-09-25  2863.126  2901.419  2952.451  2889.048  2896.306   \n",
       "8248 2024-09-26  2896.306  2893.745  3000.953  2889.014  3000.953   \n",
       "8249 2024-09-27  3000.953  3049.103  3087.529  3017.445  3087.529   \n",
       "8250 2024-09-30  3087.529  3194.722  3358.588  3153.697  3336.497   \n",
       "\n",
       "           Volume         Money  \n",
       "0       126000.00  4.940000e+05  \n",
       "1        19700.00  8.400000e+04  \n",
       "2         2800.00  1.600000e+04  \n",
       "3         3200.00  3.100000e+04  \n",
       "4         1500.00  6.000000e+03  \n",
       "...           ...           ...  \n",
       "8246   4776195.45  4.427953e+07  \n",
       "8247   5682598.16  5.166981e+07  \n",
       "8248   5763192.61  5.246691e+07  \n",
       "8249   4922871.63  4.806126e+07  \n",
       "8250  11023379.17  1.167773e+08  \n",
       "\n",
       "[8251 rows x 8 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Day']=pd.to_datetime(data['Day'],format='%Y/%m/%d')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-12-19</th>\n",
       "      <td></td>\n",
       "      <td>96.050</td>\n",
       "      <td>99.980</td>\n",
       "      <td>95.790</td>\n",
       "      <td>99.980</td>\n",
       "      <td>126000.00</td>\n",
       "      <td>4.940000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-20</th>\n",
       "      <td>99.98</td>\n",
       "      <td>104.300</td>\n",
       "      <td>104.390</td>\n",
       "      <td>99.980</td>\n",
       "      <td>104.390</td>\n",
       "      <td>19700.00</td>\n",
       "      <td>8.400000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-21</th>\n",
       "      <td>104.39</td>\n",
       "      <td>109.070</td>\n",
       "      <td>109.130</td>\n",
       "      <td>103.730</td>\n",
       "      <td>109.130</td>\n",
       "      <td>2800.00</td>\n",
       "      <td>1.600000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-24</th>\n",
       "      <td>109.13</td>\n",
       "      <td>113.570</td>\n",
       "      <td>114.550</td>\n",
       "      <td>109.130</td>\n",
       "      <td>114.550</td>\n",
       "      <td>3200.00</td>\n",
       "      <td>3.100000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-25</th>\n",
       "      <td>114.55</td>\n",
       "      <td>120.090</td>\n",
       "      <td>120.250</td>\n",
       "      <td>114.550</td>\n",
       "      <td>120.250</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>6.000000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-24</th>\n",
       "      <td>2748.918</td>\n",
       "      <td>2770.754</td>\n",
       "      <td>2863.152</td>\n",
       "      <td>2761.372</td>\n",
       "      <td>2863.126</td>\n",
       "      <td>4776195.45</td>\n",
       "      <td>4.427953e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-25</th>\n",
       "      <td>2863.126</td>\n",
       "      <td>2901.419</td>\n",
       "      <td>2952.451</td>\n",
       "      <td>2889.048</td>\n",
       "      <td>2896.306</td>\n",
       "      <td>5682598.16</td>\n",
       "      <td>5.166981e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-26</th>\n",
       "      <td>2896.306</td>\n",
       "      <td>2893.745</td>\n",
       "      <td>3000.953</td>\n",
       "      <td>2889.014</td>\n",
       "      <td>3000.953</td>\n",
       "      <td>5763192.61</td>\n",
       "      <td>5.246691e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-27</th>\n",
       "      <td>3000.953</td>\n",
       "      <td>3049.103</td>\n",
       "      <td>3087.529</td>\n",
       "      <td>3017.445</td>\n",
       "      <td>3087.529</td>\n",
       "      <td>4922871.63</td>\n",
       "      <td>4.806126e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-30</th>\n",
       "      <td>3087.529</td>\n",
       "      <td>3194.722</td>\n",
       "      <td>3358.588</td>\n",
       "      <td>3153.697</td>\n",
       "      <td>3336.497</td>\n",
       "      <td>11023379.17</td>\n",
       "      <td>1.167773e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8251 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Preclose      Open   Highest    Lowest     Close       Volume  \\\n",
       "Day                                                                         \n",
       "1990-12-19              96.050    99.980    95.790    99.980    126000.00   \n",
       "1990-12-20     99.98   104.300   104.390    99.980   104.390     19700.00   \n",
       "1990-12-21    104.39   109.070   109.130   103.730   109.130      2800.00   \n",
       "1990-12-24    109.13   113.570   114.550   109.130   114.550      3200.00   \n",
       "1990-12-25    114.55   120.090   120.250   114.550   120.250      1500.00   \n",
       "...              ...       ...       ...       ...       ...          ...   \n",
       "2024-09-24  2748.918  2770.754  2863.152  2761.372  2863.126   4776195.45   \n",
       "2024-09-25  2863.126  2901.419  2952.451  2889.048  2896.306   5682598.16   \n",
       "2024-09-26  2896.306  2893.745  3000.953  2889.014  3000.953   5763192.61   \n",
       "2024-09-27  3000.953  3049.103  3087.529  3017.445  3087.529   4922871.63   \n",
       "2024-09-30  3087.529  3194.722  3358.588  3153.697  3336.497  11023379.17   \n",
       "\n",
       "                   Money  \n",
       "Day                       \n",
       "1990-12-19  4.940000e+05  \n",
       "1990-12-20  8.400000e+04  \n",
       "1990-12-21  1.600000e+04  \n",
       "1990-12-24  3.100000e+04  \n",
       "1990-12-25  6.000000e+03  \n",
       "...                  ...  \n",
       "2024-09-24  4.427953e+07  \n",
       "2024-09-25  5.166981e+07  \n",
       "2024-09-26  5.246691e+07  \n",
       "2024-09-27  4.806126e+07  \n",
       "2024-09-30  1.167773e+08  \n",
       "\n",
       "[8251 rows x 7 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.set_index('Day',inplace=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Preclose</th>\n",
       "      <th>Open</th>\n",
       "      <th>Highest</th>\n",
       "      <th>Lowest</th>\n",
       "      <th>Close</th>\n",
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       "      <th>Money</th>\n",
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       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-12-19</th>\n",
       "      <td></td>\n",
       "      <td>96.050</td>\n",
       "      <td>99.980</td>\n",
       "      <td>95.790</td>\n",
       "      <td>99.980</td>\n",
       "      <td>126000.00</td>\n",
       "      <td>4.940000e+05</td>\n",
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       "    <tr>\n",
       "      <th>1990-12-20</th>\n",
       "      <td>99.98</td>\n",
       "      <td>104.300</td>\n",
       "      <td>104.390</td>\n",
       "      <td>99.980</td>\n",
       "      <td>104.390</td>\n",
       "      <td>19700.00</td>\n",
       "      <td>8.400000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-21</th>\n",
       "      <td>104.39</td>\n",
       "      <td>109.070</td>\n",
       "      <td>109.130</td>\n",
       "      <td>103.730</td>\n",
       "      <td>109.130</td>\n",
       "      <td>2800.00</td>\n",
       "      <td>1.600000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-24</th>\n",
       "      <td>109.13</td>\n",
       "      <td>113.570</td>\n",
       "      <td>114.550</td>\n",
       "      <td>109.130</td>\n",
       "      <td>114.550</td>\n",
       "      <td>3200.00</td>\n",
       "      <td>3.100000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-25</th>\n",
       "      <td>114.55</td>\n",
       "      <td>120.090</td>\n",
       "      <td>120.250</td>\n",
       "      <td>114.550</td>\n",
       "      <td>120.250</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>6.000000e+03</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>2024-09-24</th>\n",
       "      <td>2748.918</td>\n",
       "      <td>2770.754</td>\n",
       "      <td>2863.152</td>\n",
       "      <td>2761.372</td>\n",
       "      <td>2863.126</td>\n",
       "      <td>4776195.45</td>\n",
       "      <td>4.427953e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-25</th>\n",
       "      <td>2863.126</td>\n",
       "      <td>2901.419</td>\n",
       "      <td>2952.451</td>\n",
       "      <td>2889.048</td>\n",
       "      <td>2896.306</td>\n",
       "      <td>5682598.16</td>\n",
       "      <td>5.166981e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-26</th>\n",
       "      <td>2896.306</td>\n",
       "      <td>2893.745</td>\n",
       "      <td>3000.953</td>\n",
       "      <td>2889.014</td>\n",
       "      <td>3000.953</td>\n",
       "      <td>5763192.61</td>\n",
       "      <td>5.246691e+07</td>\n",
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       "    <tr>\n",
       "      <th>2024-09-27</th>\n",
       "      <td>3000.953</td>\n",
       "      <td>3049.103</td>\n",
       "      <td>3087.529</td>\n",
       "      <td>3017.445</td>\n",
       "      <td>3087.529</td>\n",
       "      <td>4922871.63</td>\n",
       "      <td>4.806126e+07</td>\n",
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       "    <tr>\n",
       "      <th>2024-09-30</th>\n",
       "      <td>3087.529</td>\n",
       "      <td>3194.722</td>\n",
       "      <td>3358.588</td>\n",
       "      <td>3153.697</td>\n",
       "      <td>3336.497</td>\n",
       "      <td>11023379.17</td>\n",
       "      <td>1.167773e+08</td>\n",
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       "</table>\n",
       "<p>8251 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Preclose      Open   Highest    Lowest     Close       Volume  \\\n",
       "Day                                                                         \n",
       "1990-12-19              96.050    99.980    95.790    99.980    126000.00   \n",
       "1990-12-20     99.98   104.300   104.390    99.980   104.390     19700.00   \n",
       "1990-12-21    104.39   109.070   109.130   103.730   109.130      2800.00   \n",
       "1990-12-24    109.13   113.570   114.550   109.130   114.550      3200.00   \n",
       "1990-12-25    114.55   120.090   120.250   114.550   120.250      1500.00   \n",
       "...              ...       ...       ...       ...       ...          ...   \n",
       "2024-09-24  2748.918  2770.754  2863.152  2761.372  2863.126   4776195.45   \n",
       "2024-09-25  2863.126  2901.419  2952.451  2889.048  2896.306   5682598.16   \n",
       "2024-09-26  2896.306  2893.745  3000.953  2889.014  3000.953   5763192.61   \n",
       "2024-09-27  3000.953  3049.103  3087.529  3017.445  3087.529   4922871.63   \n",
       "2024-09-30  3087.529  3194.722  3358.588  3153.697  3336.497  11023379.17   \n",
       "\n",
       "                   Money  \n",
       "Day                       \n",
       "1990-12-19  4.940000e+05  \n",
       "1990-12-20  8.400000e+04  \n",
       "1990-12-21  1.600000e+04  \n",
       "1990-12-24  3.100000e+04  \n",
       "1990-12-25  6.000000e+03  \n",
       "...                  ...  \n",
       "2024-09-24  4.427953e+07  \n",
       "2024-09-25  5.166981e+07  \n",
       "2024-09-26  5.246691e+07  \n",
       "2024-09-27  4.806126e+07  \n",
       "2024-09-30  1.167773e+08  \n",
       "\n",
       "[8251 rows x 7 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['1990':'2024']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Lowest</th>\n",
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       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-12-19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>96.050</td>\n",
       "      <td>99.980</td>\n",
       "      <td>95.790</td>\n",
       "      <td>99.980</td>\n",
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       "    <tr>\n",
       "      <th>1990-12-20</th>\n",
       "      <td>99.980</td>\n",
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       "      <td>104.390</td>\n",
       "      <td>99.980</td>\n",
       "      <td>104.390</td>\n",
       "      <td>19700.00</td>\n",
       "      <td>8.400000e+04</td>\n",
       "      <td>0.044109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-21</th>\n",
       "      <td>104.390</td>\n",
       "      <td>109.070</td>\n",
       "      <td>109.130</td>\n",
       "      <td>103.730</td>\n",
       "      <td>109.130</td>\n",
       "      <td>2800.00</td>\n",
       "      <td>1.600000e+04</td>\n",
       "      <td>0.045407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-24</th>\n",
       "      <td>109.130</td>\n",
       "      <td>113.570</td>\n",
       "      <td>114.550</td>\n",
       "      <td>109.130</td>\n",
       "      <td>114.550</td>\n",
       "      <td>3200.00</td>\n",
       "      <td>3.100000e+04</td>\n",
       "      <td>0.049666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-25</th>\n",
       "      <td>114.550</td>\n",
       "      <td>120.090</td>\n",
       "      <td>120.250</td>\n",
       "      <td>114.550</td>\n",
       "      <td>120.250</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>6.000000e+03</td>\n",
       "      <td>0.049760</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-24</th>\n",
       "      <td>2748.918</td>\n",
       "      <td>2770.754</td>\n",
       "      <td>2863.152</td>\n",
       "      <td>2761.372</td>\n",
       "      <td>2863.126</td>\n",
       "      <td>4776195.45</td>\n",
       "      <td>4.427953e+07</td>\n",
       "      <td>0.041547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-25</th>\n",
       "      <td>2863.126</td>\n",
       "      <td>2901.419</td>\n",
       "      <td>2952.451</td>\n",
       "      <td>2889.048</td>\n",
       "      <td>2896.306</td>\n",
       "      <td>5682598.16</td>\n",
       "      <td>5.166981e+07</td>\n",
       "      <td>0.011589</td>\n",
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       "    <tr>\n",
       "      <th>2024-09-26</th>\n",
       "      <td>2896.306</td>\n",
       "      <td>2893.745</td>\n",
       "      <td>3000.953</td>\n",
       "      <td>2889.014</td>\n",
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       "      <td>0.036131</td>\n",
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       "    <tr>\n",
       "      <th>2024-09-27</th>\n",
       "      <td>3000.953</td>\n",
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       "      <td>3017.445</td>\n",
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       "      <td>4.806126e+07</td>\n",
       "      <td>0.028850</td>\n",
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       "    <tr>\n",
       "      <th>2024-09-30</th>\n",
       "      <td>3087.529</td>\n",
       "      <td>3194.722</td>\n",
       "      <td>3358.588</td>\n",
       "      <td>3153.697</td>\n",
       "      <td>3336.497</td>\n",
       "      <td>11023379.17</td>\n",
       "      <td>1.167773e+08</td>\n",
       "      <td>0.080637</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8251 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Preclose      Open   Highest    Lowest     Close       Volume  \\\n",
       "Day                                                                         \n",
       "1990-12-19       NaN    96.050    99.980    95.790    99.980    126000.00   \n",
       "1990-12-20    99.980   104.300   104.390    99.980   104.390     19700.00   \n",
       "1990-12-21   104.390   109.070   109.130   103.730   109.130      2800.00   \n",
       "1990-12-24   109.130   113.570   114.550   109.130   114.550      3200.00   \n",
       "1990-12-25   114.550   120.090   120.250   114.550   120.250      1500.00   \n",
       "...              ...       ...       ...       ...       ...          ...   \n",
       "2024-09-24  2748.918  2770.754  2863.152  2761.372  2863.126   4776195.45   \n",
       "2024-09-25  2863.126  2901.419  2952.451  2889.048  2896.306   5682598.16   \n",
       "2024-09-26  2896.306  2893.745  3000.953  2889.014  3000.953   5763192.61   \n",
       "2024-09-27  3000.953  3049.103  3087.529  3017.445  3087.529   4922871.63   \n",
       "2024-09-30  3087.529  3194.722  3358.588  3153.697  3336.497  11023379.17   \n",
       "\n",
       "                   Money    Return  \n",
       "Day                                 \n",
       "1990-12-19  4.940000e+05       NaN  \n",
       "1990-12-20  8.400000e+04  0.044109  \n",
       "1990-12-21  1.600000e+04  0.045407  \n",
       "1990-12-24  3.100000e+04  0.049666  \n",
       "1990-12-25  6.000000e+03  0.049760  \n",
       "...                  ...       ...  \n",
       "2024-09-24  4.427953e+07  0.041547  \n",
       "2024-09-25  5.166981e+07  0.011589  \n",
       "2024-09-26  5.246691e+07  0.036131  \n",
       "2024-09-27  4.806126e+07  0.028850  \n",
       "2024-09-30  1.167773e+08  0.080637  \n",
       "\n",
       "[8251 rows x 8 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Close']=pd.to_numeric(data['Close'])\n",
    "data['Preclose']=data['Close'].shift(1)\n",
    "data['Return']=(data['Close'] / data['Preclose'])-1\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block (1122662939.py, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"C:\\Users\\18050\\AppData\\Local\\Temp\\ipykernel_36392\\1122662939.py\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m    data['Return_2'][i]=(data['Close'][i] / data[\"Preclose\"][i])- 1\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m expected an indented block\n"
     ]
    }
   ],
   "source": [
    "for i in range(0, len(data)):\n",
    "data['Return_2'][i]=(data['Close'][i] / data[\"Preclose\"][i])- 1\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Preclose</th>\n",
       "      <th>Open</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-03</th>\n",
       "      <td>647.8700</td>\n",
       "      <td>637.7200</td>\n",
       "      <td>647.7100</td>\n",
       "      <td>630.5300</td>\n",
       "      <td>639.8800</td>\n",
       "      <td>23451800</td>\n",
       "      <td>1.806930e+08</td>\n",
       "      <td>-0.012333</td>\n",
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       "    <tr>\n",
       "      <th>1995-01-04</th>\n",
       "      <td>639.8800</td>\n",
       "      <td>641.9000</td>\n",
       "      <td>655.5100</td>\n",
       "      <td>638.8600</td>\n",
       "      <td>653.8100</td>\n",
       "      <td>42222000</td>\n",
       "      <td>3.069230e+08</td>\n",
       "      <td>0.021770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-05</th>\n",
       "      <td>653.8100</td>\n",
       "      <td>655.3800</td>\n",
       "      <td>657.5200</td>\n",
       "      <td>645.8100</td>\n",
       "      <td>646.8900</td>\n",
       "      <td>43012300</td>\n",
       "      <td>3.015330e+08</td>\n",
       "      <td>-0.010584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-06</th>\n",
       "      <td>646.8900</td>\n",
       "      <td>642.7500</td>\n",
       "      <td>643.8900</td>\n",
       "      <td>636.3300</td>\n",
       "      <td>640.7600</td>\n",
       "      <td>48748200</td>\n",
       "      <td>3.537580e+08</td>\n",
       "      <td>-0.009476</td>\n",
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       "    <tr>\n",
       "      <th>1995-01-09</th>\n",
       "      <td>640.7600</td>\n",
       "      <td>637.5200</td>\n",
       "      <td>637.5500</td>\n",
       "      <td>625.0400</td>\n",
       "      <td>626.0000</td>\n",
       "      <td>50985100</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2024-07-25</th>\n",
       "      <td>2901.9518</td>\n",
       "      <td>2891.8918</td>\n",
       "      <td>2897.7674</td>\n",
       "      <td>2872.8497</td>\n",
       "      <td>2886.7416</td>\n",
       "      <td>27463950000</td>\n",
       "      <td>2.732820e+11</td>\n",
       "      <td>-0.005241</td>\n",
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       "    <tr>\n",
       "      <th>2024-07-26</th>\n",
       "      <td>2886.7416</td>\n",
       "      <td>2885.9953</td>\n",
       "      <td>2899.1162</td>\n",
       "      <td>2875.3959</td>\n",
       "      <td>2890.8973</td>\n",
       "      <td>27838753600</td>\n",
       "      <td>2.754430e+11</td>\n",
       "      <td>0.001440</td>\n",
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       "    <tr>\n",
       "      <th>2024-07-29</th>\n",
       "      <td>2890.8973</td>\n",
       "      <td>2889.4726</td>\n",
       "      <td>2898.9512</td>\n",
       "      <td>2878.5825</td>\n",
       "      <td>2891.8453</td>\n",
       "      <td>25689972700</td>\n",
       "      <td>2.600950e+11</td>\n",
       "      <td>0.000328</td>\n",
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       "    <tr>\n",
       "      <th>2024-07-30</th>\n",
       "      <td>2891.8453</td>\n",
       "      <td>2885.2152</td>\n",
       "      <td>2885.2152</td>\n",
       "      <td>2865.1493</td>\n",
       "      <td>2879.2996</td>\n",
       "      <td>26247883700</td>\n",
       "      <td>2.694770e+11</td>\n",
       "      <td>-0.004338</td>\n",
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       "    <tr>\n",
       "      <th>2024-07-31</th>\n",
       "      <td>2879.2996</td>\n",
       "      <td>2877.5409</td>\n",
       "      <td>2940.5927</td>\n",
       "      <td>2876.3009</td>\n",
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       "      <td>4.188720e+11</td>\n",
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       "             Preclose       Open    Highest     Lowest      Close  \\\n",
       "Day                                                                 \n",
       "1995-01-03   647.8700   637.7200   647.7100   630.5300   639.8800   \n",
       "1995-01-04   639.8800   641.9000   655.5100   638.8600   653.8100   \n",
       "1995-01-05   653.8100   655.3800   657.5200   645.8100   646.8900   \n",
       "1995-01-06   646.8900   642.7500   643.8900   636.3300   640.7600   \n",
       "1995-01-09   640.7600   637.5200   637.5500   625.0400   626.0000   \n",
       "...               ...        ...        ...        ...        ...   \n",
       "2024-07-25  2901.9518  2891.8918  2897.7674  2872.8497  2886.7416   \n",
       "2024-07-26  2886.7416  2885.9953  2899.1162  2875.3959  2890.8973   \n",
       "2024-07-29  2890.8973  2889.4726  2898.9512  2878.5825  2891.8453   \n",
       "2024-07-30  2891.8453  2885.2152  2885.2152  2865.1493  2879.2996   \n",
       "2024-07-31  2879.2996  2877.5409  2940.5927  2876.3009  2938.7493   \n",
       "\n",
       "                 Volume         Money    Return  \n",
       "Day                                              \n",
       "1995-01-03     23451800  1.806930e+08 -0.012333  \n",
       "1995-01-04     42222000  3.069230e+08  0.021770  \n",
       "1995-01-05     43012300  3.015330e+08 -0.010584  \n",
       "1995-01-06     48748200  3.537580e+08 -0.009476  \n",
       "1995-01-09     50985100  3.985190e+08 -0.023035  \n",
       "...                 ...           ...       ...  \n",
       "2024-07-25  27463950000  2.732820e+11 -0.005241  \n",
       "2024-07-26  27838753600  2.754430e+11  0.001440  \n",
       "2024-07-29  25689972700  2.600950e+11  0.000328  \n",
       "2024-07-30  26247883700  2.694770e+11 -0.004338  \n",
       "2024-07-31  41272341700  4.188720e+11  0.020647  \n",
       "\n",
       "[7182 rows x 8 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new=data['1995':'2024'].copy()\n",
    "data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-31</th>\n",
       "      <td>562.5900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-02-28</th>\n",
       "      <td>549.2600</td>\n",
       "      <td>562.5900</td>\n",
       "      <td>-0.023694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-03-31</th>\n",
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       "      <th>1995-05-31</th>\n",
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       "      <td>579.9300</td>\n",
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       "      <th>2024-03-31</th>\n",
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       "      <td>0.008622</td>\n",
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       "    <tr>\n",
       "      <th>2024-04-30</th>\n",
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       "      <td>3041.1669</td>\n",
       "      <td>0.020932</td>\n",
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       "    <tr>\n",
       "      <th>2024-05-31</th>\n",
       "      <td>3086.8134</td>\n",
       "      <td>3104.8245</td>\n",
       "      <td>-0.005801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-30</th>\n",
       "      <td>2967.4028</td>\n",
       "      <td>3086.8134</td>\n",
       "      <td>-0.038684</td>\n",
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       "    <tr>\n",
       "      <th>2024-07-31</th>\n",
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       "</table>\n",
       "<p>355 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Close   Preclose    Return\n",
       "Day                                       \n",
       "1995-01-31   562.5900        NaN       NaN\n",
       "1995-02-28   549.2600   562.5900 -0.023694\n",
       "1995-03-31   646.9200   549.2600  0.177803\n",
       "1995-04-30   579.9300   646.9200 -0.103552\n",
       "1995-05-31   700.5100   579.9300  0.207922\n",
       "...               ...        ...       ...\n",
       "2024-03-31  3041.1669  3015.1712  0.008622\n",
       "2024-04-30  3104.8245  3041.1669  0.020932\n",
       "2024-05-31  3086.8134  3104.8245 -0.005801\n",
       "2024-06-30  2967.4028  3086.8134 -0.038684\n",
       "2024-07-31  2938.7493  2967.4028 -0.009656\n",
       "\n",
       "[355 rows x 3 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Month_data=data_new.resample('M')['Close'].last().to_frame()\n",
    "Month_data['Preclose']=Month_data['Close'].shift(1)\n",
    "Month_data['Return']=(Month_data.Close/Month_data.Preclose)-1\n",
    "Month_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1995-04-30</th>\n",
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      "text/plain": [
       "                Close\n",
       "Day                  \n",
       "1995-01-31   562.5900\n",
       "1995-02-28   549.2600\n",
       "1995-03-31   646.9200\n",
       "1995-04-30   579.9300\n",
       "1995-05-31   700.5100\n",
       "...               ...\n",
       "2024-03-31  3041.1669\n",
       "2024-04-30  3104.8245\n",
       "2024-05-31  3086.8134\n",
       "2024-06-30  2967.4028\n",
       "2024-07-31  2938.7493\n",
       "\n",
       "[355 rows x 1 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "Month_data=data_new.resample('M')['Close'].last().to_frame()\n",
    "Month_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-31</th>\n",
       "      <td>4.889181e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-02-28</th>\n",
       "      <td>1.297918e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-03-31</th>\n",
       "      <td>9.118688e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-04-30</th>\n",
       "      <td>9.938539e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-05-31</th>\n",
       "      <td>4.684472e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-31</th>\n",
       "      <td>2.106394e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-30</th>\n",
       "      <td>1.059696e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-31</th>\n",
       "      <td>1.045649e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-30</th>\n",
       "      <td>5.140876e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-31</th>\n",
       "      <td>8.349506e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>355 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Return\n",
       "Day                     \n",
       "1995-01-31  4.889181e+05\n",
       "1995-02-28  1.297918e+05\n",
       "1995-03-31  9.118688e+06\n",
       "1995-04-30  9.938539e+05\n",
       "1995-05-31  4.684472e+06\n",
       "...                  ...\n",
       "2024-03-31  2.106394e+06\n",
       "2024-04-30  1.059696e+06\n",
       "2024-05-31  1.045649e+06\n",
       "2024-06-30  5.140876e+05\n",
       "2024-07-31  8.349506e+06\n",
       "\n",
       "[355 rows x 1 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new.Return=data_new.Return+1\n",
    "Month_data=data_new.resample(\"M\")[\"Return\"].prod().to_frame()\n",
    "Month_data=Month_data-1\n",
    "Month_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Return</th>\n",
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       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-31</th>\n",
       "      <td>1.109561e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-02-28</th>\n",
       "      <td>1.283617e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-03-31</th>\n",
       "      <td>9.956561e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-04-30</th>\n",
       "      <td>3.365181e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-05-31</th>\n",
       "      <td>3.390827e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-31</th>\n",
       "      <td>1.049131e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-30</th>\n",
       "      <td>3.511543e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-31</th>\n",
       "      <td>3.480383e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-30</th>\n",
       "      <td>1.147159e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-31</th>\n",
       "      <td>9.385415e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>355 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Return\n",
       "Day                     \n",
       "1995-01-31  1.109561e+09\n",
       "1995-02-28  1.283617e+08\n",
       "1995-03-31  9.956561e+10\n",
       "1995-04-30  3.365181e+09\n",
       "1995-05-31  3.390827e+10\n",
       "...                  ...\n",
       "2024-03-31  1.049131e+10\n",
       "2024-04-30  3.511543e+09\n",
       "2024-05-31  3.480383e+09\n",
       "2024-06-30  1.147159e+09\n",
       "2024-07-31  9.385415e+10\n",
       "\n",
       "[355 rows x 1 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new.Return=data_new.Return+1\n",
    "Month_data=data_new.resample(\"M\")[\"Return\"].prod().to_frame()\n",
    "Month_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Return</th>\n",
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       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-12-31</th>\n",
       "      <td>-1.0</td>\n",
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       "    <tr>\n",
       "      <th>1996-12-31</th>\n",
       "      <td>-1.0</td>\n",
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       "    <tr>\n",
       "      <th>1997-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-12-31</th>\n",
       "      <td>-1.0</td>\n",
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       "    <tr>\n",
       "      <th>2001-12-31</th>\n",
       "      <td>-1.0</td>\n",
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       "    <tr>\n",
       "      <th>2002-12-31</th>\n",
       "      <td>-1.0</td>\n",
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       "    <tr>\n",
       "      <th>2003-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-31</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Return\n",
       "Day               \n",
       "1995-12-31    -1.0\n",
       "1996-12-31    -1.0\n",
       "1997-12-31    -1.0\n",
       "1998-12-31    -1.0\n",
       "1999-12-31    -1.0\n",
       "2000-12-31    -1.0\n",
       "2001-12-31    -1.0\n",
       "2002-12-31    -1.0\n",
       "2003-12-31    -1.0\n",
       "2004-12-31    -1.0\n",
       "2005-12-31    -1.0\n",
       "2006-12-31    -1.0\n",
       "2007-12-31    -1.0\n",
       "2008-12-31    -1.0\n",
       "2009-12-31    -1.0\n",
       "2010-12-31    -1.0\n",
       "2011-12-31    -1.0\n",
       "2012-12-31    -1.0\n",
       "2013-12-31    -1.0\n",
       "2014-12-31    -1.0\n",
       "2015-12-31    -1.0\n",
       "2016-12-31    -1.0\n",
       "2017-12-31    -1.0\n",
       "2018-12-31    -1.0\n",
       "2019-12-31    -1.0\n",
       "2020-12-31    -1.0\n",
       "2021-12-31    -1.0\n",
       "2022-12-31    -1.0\n",
       "2023-12-31    -1.0\n",
       "2024-12-31    -1.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new=data['1995':'2024'].copy()\n",
    "data_new.Return=data_new.Return\n",
    "Year_data=data_new.resample('Y')['Return'].prod().to_frame()\n",
    "Year_data.Return=Year_data.Return-1\n",
    "Year_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib import style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Preclose</th>\n",
       "      <th>Open</th>\n",
       "      <th>Highest</th>\n",
       "      <th>Lowest</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Money</th>\n",
       "      <th>Return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-12-19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>96.0500</td>\n",
       "      <td>99.9800</td>\n",
       "      <td>95.7900</td>\n",
       "      <td>99.9800</td>\n",
       "      <td>126000</td>\n",
       "      <td>4.940000e+05</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-20</th>\n",
       "      <td>99.9800</td>\n",
       "      <td>104.3000</td>\n",
       "      <td>104.3900</td>\n",
       "      <td>99.9800</td>\n",
       "      <td>104.3900</td>\n",
       "      <td>19700</td>\n",
       "      <td>8.400000e+04</td>\n",
       "      <td>0.044109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-21</th>\n",
       "      <td>104.3900</td>\n",
       "      <td>109.0700</td>\n",
       "      <td>109.1300</td>\n",
       "      <td>103.7300</td>\n",
       "      <td>109.1300</td>\n",
       "      <td>2800</td>\n",
       "      <td>1.600000e+04</td>\n",
       "      <td>0.045407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-24</th>\n",
       "      <td>109.1300</td>\n",
       "      <td>113.5700</td>\n",
       "      <td>114.5500</td>\n",
       "      <td>109.1300</td>\n",
       "      <td>114.5500</td>\n",
       "      <td>3200</td>\n",
       "      <td>3.100000e+04</td>\n",
       "      <td>0.049666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-25</th>\n",
       "      <td>114.5500</td>\n",
       "      <td>120.0900</td>\n",
       "      <td>120.2500</td>\n",
       "      <td>114.5500</td>\n",
       "      <td>120.2500</td>\n",
       "      <td>1500</td>\n",
       "      <td>6.000000e+03</td>\n",
       "      <td>0.049760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-25</th>\n",
       "      <td>2901.9518</td>\n",
       "      <td>2891.8918</td>\n",
       "      <td>2897.7674</td>\n",
       "      <td>2872.8497</td>\n",
       "      <td>2886.7416</td>\n",
       "      <td>27463950000</td>\n",
       "      <td>2.732820e+11</td>\n",
       "      <td>-0.005241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-26</th>\n",
       "      <td>2886.7416</td>\n",
       "      <td>2885.9953</td>\n",
       "      <td>2899.1162</td>\n",
       "      <td>2875.3959</td>\n",
       "      <td>2890.8973</td>\n",
       "      <td>27838753600</td>\n",
       "      <td>2.754430e+11</td>\n",
       "      <td>0.001440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-29</th>\n",
       "      <td>2890.8973</td>\n",
       "      <td>2889.4726</td>\n",
       "      <td>2898.9512</td>\n",
       "      <td>2878.5825</td>\n",
       "      <td>2891.8453</td>\n",
       "      <td>25689972700</td>\n",
       "      <td>2.600950e+11</td>\n",
       "      <td>0.000328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-30</th>\n",
       "      <td>2891.8453</td>\n",
       "      <td>2885.2152</td>\n",
       "      <td>2885.2152</td>\n",
       "      <td>2865.1493</td>\n",
       "      <td>2879.2996</td>\n",
       "      <td>26247883700</td>\n",
       "      <td>2.694770e+11</td>\n",
       "      <td>-0.004338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-31</th>\n",
       "      <td>2879.2996</td>\n",
       "      <td>2877.5409</td>\n",
       "      <td>2940.5927</td>\n",
       "      <td>2876.3009</td>\n",
       "      <td>2938.7493</td>\n",
       "      <td>41272341700</td>\n",
       "      <td>4.188720e+11</td>\n",
       "      <td>0.020647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8210 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Preclose       Open    Highest     Lowest      Close  \\\n",
       "Day                                                                 \n",
       "1990-12-19        NaN    96.0500    99.9800    95.7900    99.9800   \n",
       "1990-12-20    99.9800   104.3000   104.3900    99.9800   104.3900   \n",
       "1990-12-21   104.3900   109.0700   109.1300   103.7300   109.1300   \n",
       "1990-12-24   109.1300   113.5700   114.5500   109.1300   114.5500   \n",
       "1990-12-25   114.5500   120.0900   120.2500   114.5500   120.2500   \n",
       "...               ...        ...        ...        ...        ...   \n",
       "2024-07-25  2901.9518  2891.8918  2897.7674  2872.8497  2886.7416   \n",
       "2024-07-26  2886.7416  2885.9953  2899.1162  2875.3959  2890.8973   \n",
       "2024-07-29  2890.8973  2889.4726  2898.9512  2878.5825  2891.8453   \n",
       "2024-07-30  2891.8453  2885.2152  2885.2152  2865.1493  2879.2996   \n",
       "2024-07-31  2879.2996  2877.5409  2940.5927  2876.3009  2938.7493   \n",
       "\n",
       "                 Volume         Money    Return  \n",
       "Day                                              \n",
       "1990-12-19       126000  4.940000e+05       NaN  \n",
       "1990-12-20        19700  8.400000e+04  0.044109  \n",
       "1990-12-21         2800  1.600000e+04  0.045407  \n",
       "1990-12-24         3200  3.100000e+04  0.049666  \n",
       "1990-12-25         1500  6.000000e+03  0.049760  \n",
       "...                 ...           ...       ...  \n",
       "2024-07-25  27463950000  2.732820e+11 -0.005241  \n",
       "2024-07-26  27838753600  2.754430e+11  0.001440  \n",
       "2024-07-29  25689972700  2.600950e+11  0.000328  \n",
       "2024-07-30  26247883700  2.694770e+11 -0.004338  \n",
       "2024-07-31  41272341700  4.188720e+11  0.020647  \n",
       "\n",
       "[8210 rows x 8 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"000001.csv\")\n",
    "data['Day']=pd.to_datetime(data['Day'],format='%Y/%m/%d')\n",
    "data.set_index('Day',inplace=True)\n",
    "data[\"Close\"]=pd.to_numeric(data[\"Close\"])\n",
    "data[\"Preclose\"]=data[\"Close\"].shift(1)\n",
    "#pandas中shift用于将数据向上或者向下移动指定行数\n",
    "data[\"Return\"]=(data.Close/data.Preclose)-1\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_new = data['1995':'2024'].copy()\n",
    "Month_data=data_new.resample('M')['Close'].last().to_frame()\n",
    "#last()取每个月的最后一天来算\n",
    "Month_data['Preclose']=Month_data['Close'].shift(1)\n",
    "Month_data['Return']=(Month_data.Close/Month_data.Preclose)-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(9,4))\n",
    "ax.plot('Return',\n",
    "        '^-',\n",
    "        label=\"Monthly Return\",\n",
    "        color=\"r\",\n",
    "        linewidth=1,\n",
    "        data=Month_data['1995':'2024'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_36392\\2389712933.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mMonth_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Return'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "np.mean(Month_data['Return'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
